Numerical modeling of 3D woven composite reinforcements: A review

The literature of numerical modeling of 3D woven composite reinforcements shows that a wide range of impressive studies have been carried out in the last two decades. During this period, two distinct strategies have emerged: the predictive approaches that call for a mechanical construction as well as numerical simulations (e.g., FE method), and the descriptive approaches that are devoted to extracting the geometry of a real textile from micro-tomographic images. In the former methods, different geometrical and mechanical strategies have been employed for mimicking the yarn behavior at either the mesoor sub-mesoscales. And in the latter ones, different approaches ranging from ad hoc image processing pipelines up to more advanced machine learning strategies have been used but only at the mesoscale. This paper aims to highlight the advantages and ideal usecases of each method as well as for each analysis scale (meso-or sub-mesoscale). A common terminology is proposed for organizing and discussing the various mesoand sub-mesoscale strategies. It should be noted that this work only covers the modeling strategies for the textile reinforcement (i.e., dry fabric), thus mesoor macroscale analyses of complete composites are not discussed.

Automated summary

The literature on numerical modeling of 3D woven composite reinforcements has seen a wide range of studies in the last two decades, with two distinct strategies emerging: predictive approaches involving mechanical constructions and numerical simulations, and descriptive approaches focusing on extracting real textile geometry. Different strategies have been employed for yarn behavior modeling at different scales, with a proposed common terminology for organizing and discussing strategies.